In the oil and gas industry today, there are several conditions that drive the need for non-traditional methods for obtaining open hole logging data. As a result, oil and gas companies are more inclined to explore such non-traditional methods for obtaining open hole logging data to help in their decision making processes. The use of cased hole logging data, in particular pulsed neutron data to generate pseudo or artificial open hole triple combo log information is one approach which has been tried.
One of the conditions is simple economics. Every operation carried out in a borehole takes time, which translates directly to increased cost of drilling the well. Therefore, if a logging operation in the well, e.g. an open hole log, can be avoided, it reduces the cost of drilling the well. If the same data can be obtained from another operation, e.g. a cased hole pulsed neutron log, then the actual open hole log can be skipped, saving time and money.
Adverse drilling conditions often make open hole logging expensive, risky or essentially impossible. Such conditions include extreme wash outs, shale bridges, caving, etc. These conditions may make it physically impossible to run an open hole logging tool in the hole. If the tool can be run, the conditions may prevent collection of useful data in at least portions of the well.
Modern drilling techniques may make open hole logging risky or impossible. For example highly deviated wells may have high rates of turn or high angles which make it difficult or impossible to run an open hole tool. Some companies use slim holes, e.g. 3.5 inch diameter wells, which are too small for available open hole logging tools. However, pulsed neutron logging tools are available for running in such wells after they are cased.
As a result of these conditions, efforts have been made to produce synthetic or artificial open hole type logs from real data taken by pulsed neutron logging tools. However, various difficulties have been encountered in developing the predictive tools or models which are used to create such synthetic logs. For this approach to be successful, the models must produce accurate synthetic logs which can be relied on.
Various predictive tools have been used in processing geological logging data for many years. A field data based predictive model usually takes selected measurements of specific logging tools as inputs and produces predicted outputs using either a deterministic function or an empirical function generated from a training process. As a typical predictive framework, the artificial neural network (ANN) has received special interest and demonstrates increased use in petrophysical applications. To build an ANN model, data are selected from well logs, trained with optimization algorithms, and tested in different wells for validation. In the course of this process, data selection not only produces the greatest impact on the scope and applicability of the model, but also affects its accuracy and generalization performance. This is especially true if a single model for the field/reservoir is desired, and the data for all training wells and testing wells need to be normalized to a “field histogram”. Since the uncertainty induced by different environmental factors and/or systematic errors may somehow corrupt the field data integration and pre-processing, special attention and treatment should be given to training-data selection.
The training-data selection is more heuristic than systematic in most neural network applications. One of the common heuristic approaches is to use a predetermined data percentage to randomly select the training, validation and testing data sets, which may cause the training results to be sensitive to the specific data splitting, especially if only single well data is available. For multiple-well training-data selection, it is quite often the case to define a resampling strategy to remove a certain amount of data in each individual well, and make the combined data set fall within a specific size limit. This procedure allows the use of some powerful, but memory-constrained training algorithms (Levenberg-Marquardt-based algorithms, for example). Otherwise, some sub-optimal training algorithms (gradient-descent-based algorithms) must be used with sacrificed training accuracy. However, as discussed above, decision-making is difficult in determining the resampling strategy without a deep understanding of the nature of the multiple well data. Evenly scattered interval sampling (systematic sampling with respect to depth) with reduced density may remove some redundant data, but may also remove some useful information at the same time such as thin bed data.
There is a tendency today to integrate ANN technology with other data mining and artificial intelligence technologies for predictive model development. The advantages of using integrated technologies include enhanced predictability of the data, improved interpretability of the results, and extended applicability of the model. However, its trade-off with processing complexity should also be considered.
It would be desirable to have ways (1) remove faulty, redundant and insignificant data, (2) detect inconsistent data, (3) have the ability to “add”, i.e., duplicate samples in key target zones.